Contrastive Learning

Contrastive Learning is a deep learning technique for unsupervised representation learning. The goal is to learn a representation of data such that similar instances are close together in the representation space, while dissimilar instances are far apart.

It has been shown to be effective in various computer vision and natural language processing tasks, including image retrieval, zero-shot learning, and cross-modal retrieval. In these tasks, the learned representations can be used as features for downstream tasks such as classification and clustering.

(Image credit: Schroff et al. 2015)

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 291–300 of 6661 papers

Title	Date	Tasks	Status	Hype
Unveiling Contrastive Learning's Capability of Neighborhood Aggregation for Collaborative Filtering	Apr 14, 2025	Collaborative FilteringContrastive Learning	CodeCode Available	1
On the Value of Cross-Modal Misalignment in Multimodal Representation Learning	Apr 14, 2025	Contrastive LearningRepresentation Learning	CodeCode Available	0
Integrating Textual Embeddings from Contrastive Learning with Generative Recommender for Enhanced Personalization	Apr 13, 2025	Contrastive LearningRecommendation Systems	CodeCode Available	1
FSSUAVL: A Discriminative Framework using Vision Models for Federated Self-Supervised Audio and Image Understanding	Apr 13, 2025	Contrastive LearningFederated Learning	—Unverified	0
Multi-Modal Hypergraph Enhanced LLM Learning for Recommendation	Apr 13, 2025	Contrastive LearningMultimodal Recommendation	—Unverified	0
Causal integration of chemical structures improves representations of microscopy images for morphological profiling	Apr 13, 2025	Contrastive Learningcounterfactual	CodeCode Available	0
Federated Prototype Graph Learning	Apr 13, 2025	Contrastive LearningGraph Learning	—Unverified	0
FairACE: Achieving Degree Fairness in Graph Neural Networks via Contrastive and Adversarial Group-Balanced Training	Apr 12, 2025	Contrastive LearningFairness	—Unverified	0
CMIP-CIL: A Cross-Modal Benchmark for Image-Point Class Incremental Learning	Apr 11, 2025	class-incremental learningClass Incremental Learning	CodeCode Available	0
ProtoECGNet: Case-Based Interpretable Deep Learning for Multi-Label ECG Classification with Contrastive Learning	Apr 11, 2025	Contrastive LearningDeep Learning	CodeCode Available	1

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All datasets imagenet-1k 10,000 People - Human Pose Recognition Data CIFAR-10 STL-10

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	ResNet50	ImageNet Top-1 Accuracy	73.6	—	Unverified
2	ResNet50	ImageNet Top-1 Accuracy	73	—	Unverified
3	ResNet50	ImageNet Top-1 Accuracy	71.1	—	Unverified
4	ResNet50	ImageNet Top-1 Accuracy	69.3	—	Unverified
5	ResNet50 (v2)	ImageNet Top-1 Accuracy	67.6	—	Unverified
6	ResNet50 (v2)	ImageNet Top-1 Accuracy	63.8	—	Unverified
7	ResNet50	ImageNet Top-1 Accuracy	63.6	—	Unverified
8	ResNet50	ImageNet Top-1 Accuracy	61.5	—	Unverified
9	ResNet50	ImageNet Top-1 Accuracy	61.5	—	Unverified
10	ResNet50 (4×)	ImageNet Top-1 Accuracy	61.3	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	1	0..5sec	1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	IPCL (ResNet18)	Accuracy (Top-1)	84.77	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	IPCL (ResNet18)	Accuracy (Top-1)	85.55	—	Unverified